ASIC-Enabled Programmable Metasurfaces—Part 2: Performance and Synthesis
Bibliographic record
Abstract
A multifunctional and reconfigurable programmable metasurface (PMSF) enabled by an application-specific integrated circuit (ASIC) that targets smart wireless environment applications is presented in this work. In the accompanying paper, in Part 1, the PMSF design experimentally demonstrated the ability to control independently the magnitude and phase of the reflection coefficients for both orthogonal polarizations. Here, this ability is exploited to demonstrate programmable electromagnetic wavefront manipulation. A fast and accurate synthesis method using a 2-D single-iteration discrete Fourier transform (DFT) for producing multiple pencil beams, each with different polarizations, is presented. Each pencil beam can have a linear, right-handed circular polarization, left-handed circular polarization, or even elliptical polarization. The synthesis method is ideal for wireless reconfigurable environments enabled by the PMSF, where the number of unit cells is very large. The PMSF aims to be a single component, installed not only in the surrounding walls to reconfigure the wireless environment, but also at the transmitting antennas. The design is experimentally verified by producing a mixture of linearly polarized and circularly polarized wavefronts, and also single, dual, and simultaneous orbital angular momentum (OAM) pencil beams are experimentally shown.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".